Learning the natural grasping component of an unknown object

El-Khoury, Sahar; Sahbani, Anis; Perdereau, Véronique

doi:10.1109/iros.2007.4399052

Cited by 31 publications

(17 citation statements)

References 14 publications

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“…El-Khoury et al [67,68] consider the problem of grasping unknown objects in the same manner as humans. Based on the idea that the human brain represents objects as volumetric primitives in order to recognize them, the proposed algorithm predicts grasp as a function of the object's parts assembly.…”

Section: Systems Based On the Object Observationmentioning

confidence: 99%

An overview of 3D object grasp synthesis algorithms

Sahbani

El-Khoury

Bidaud

2012

Robotics and Autonomous Systems

404

214

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a b s t r a c tThis overview presents computational algorithms for generating 3D object grasps with autonomous multi-fingered robotic hands. Robotic grasping has been an active research subject for decades, and a great deal of effort has been spent on grasp synthesis algorithms. Existing papers focus on reviewing the mechanics of grasping and the finger-object contact interactions Bicchi and Kumar (2000) [12] or robot hand design and their control Al-Gallaf et al. (1993) [70]. Robot grasp synthesis algorithms have been reviewed in Shimoga (1996) [71], but since then an important progress has been made toward applying learning techniques to the grasping problem. This overview focuses on analytical as well as empirical grasp synthesis approaches.

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Section: Systems Based On the Object Observationmentioning

confidence: 99%

An overview of 3D object grasp synthesis algorithms

Sahbani

El-Khoury

Bidaud

2012

Robotics and Autonomous Systems

404

214

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show abstract

“…Other 3-D data sets adopt some parameterized model such as superquadrics to represent the shape of the object, which utilize geometry parameters to distinguish the graspable part from the object. 15,16 Learning good grasps from the 2-D image is also very appealing, one remarkable work 2 uses 2-D synthetic models to learn good grasp point from the image and project it to the 3-D space. Thanks to the synthetic object data sets and simulation environments, great progress has been made on the datadriven robotic grasp synthesis.…”

Section: Related Workmentioning

confidence: 99%

Deep vision networks for real-time robotic grasp detection

Guo

Sun

Kong

et al. 2016

International Journal of Advanced Robotic Systems

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Grasping has always been a great challenge for robots due to its lack of the ability to well understand the perceived sensing data. In this work, we propose an end-to-end deep vision network model to predict possible good grasps from real-world images in real time. In order to accelerate the speed of the grasp detection, reference rectangles are designed to suggest potential grasp locations and then refined to indicate robotic grasps in the image. With the proposed model, the graspable scores for each location in the image and the corresponding predicted grasp rectangles can be obtained in real time at a rate of 80 frames per second on a graphic processing unit. The model is evaluated on a real robot-collected data set and different reference rectangle settings are compared to yield the best detection performance. The experimental results demonstrate that the proposed approach can assist the robot to learn the graspable part of the object from the image in a fast manner.

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“…5) illustrates a description of the proposed grasping strategy [34]. By representing objects as a set of components, we may identify the graspable one.…”

Section: Grasping By Components: the Strategymentioning

confidence: 99%

“…With only few parameters, superquadrics can represent a large variety of standard geometric solids as well as smooth shapes. In order to have a manageable number of superquadrics shapes, we have chosen 7 representative models that span the space of superellipsoids: box, cylinder, sphere, bent box, bent cylinder, tapered box and tapered cylinder [34]. A superquadric surface model is defined by the following implicit equation:…”

Section: Approximationmentioning

confidence: 99%

A new strategy combining empirical and analytical approaches for grasping unknown 3D objects

El-Khoury¹,

Sahbani²

2010

Robotics and Autonomous Systems

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This paper proposes a novel strategy for grasping 3D unknown objects in accordance with their corresponding task. We define the handle or the natural grasping component of an object as the part chosen by humans to pick this object with. When humans reach out to grasp an object, it is generally in the aim of accomplishing a task. Thus, the chosen grasp is quite related to the object task. Our approach learns to identify objects handles by imitating humans. In this paper, a new sufficient condition for computing force-closure grasps on the obtained handle is also proposed. Several experiments were conducted to test the ability of the algorithm to generalize to new objects. They also show the adaptability of our strategy to the hand kinematics.

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Learning the natural grasping component of an unknown object

Cited by 31 publications

References 14 publications

An overview of 3D object grasp synthesis algorithms

An overview of 3D object grasp synthesis algorithms

Deep vision networks for real-time robotic grasp detection

A new strategy combining empirical and analytical approaches for grasping unknown 3D objects

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